Hi B. Sahoo
To update spyder to 4.0 follow the below steps:

You can easily install if you use Anaconda by running:

conda update qt pyqt
conda install -c spyder-ide spyder=4.0.0rc1

Hope it helps you.
Best
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On Saturday, 21 December 2019 23:18:05 UTC+5:30, Bhanupratap Sahoo wrote:
>
> Sorry I cant understand.I'm a student and new to use spyder.
> Can you tell me how can I update spyder 4 by Command Prompt or Anaconda 
> command prompt?
> *Have a good day*
> *Thank you*
>
> *Bhanupratap*
>
>
> On Sat, Dec 21, 2019 at 10:57 PM Vivek Katakam <[email protected] 
> <javascript:>> wrote:
>
>> Hi All,
>>
>> I get getting the following message at the end of output after running my 
>> mnist dataset program in the Spyder IDE:
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>> *Init signature:Dense(    units,    activation=None,    use_bias=True,    
>> kernel_initializer='glorot_uniform',    bias_initializer='zeros',    
>> kernel_regularizer=None,    bias_regularizer=None,    
>> activity_regularizer=None,    kernel_constraint=None,    
>> bias_constraint=None,    **kwargs,)Docstring:     Just your regular 
>> densely-connected NN layer.`Dense` implements the operation:`output = 
>> activation(dot(input, kernel) + bias)`where `activation` is the 
>> element-wise activation functionpassed as the `activation` argument, 
>> `kernel` is a weights matrixcreated by the layer, and `bias` is a bias 
>> vector created by the layer(only applicable if `use_bias` is `True`).Note: 
>> if the input to the layer has a rank greater than 2, thenit is flattened 
>> prior to the initial dot product with `kernel`.# Example```python    # as 
>> first layer in a sequential model:    model = Sequential()    
>> model.add(Dense(32, input_shape=(16,)))    # now the model will take as 
>> input arrays of shape (*, 16)    # and output arrays of shape (*, 32)    # 
>> after the first layer, you don't need to specify    # the size of the input 
>> anymore:    model.add(Dense(32))```# Arguments    units: Positive integer, 
>> dimensionality of the output space.    activation: Activation function to 
>> use        (see [activations](../activations.md)).        If you don't 
>> specify anything, no activation is applied        (ie. "linear" activation: 
>> `a(x) = x`).    use_bias: Boolean, whether the layer uses a bias vector.    
>> kernel_initializer: Initializer for the `kernel` weights matrix        (see 
>> [initializers](../initializers.md)).    bias_initializer: Initializer for 
>> the bias vector        (see [initializers](../initializers.md)).    
>> kernel_regularizer: Regularizer function applied to        the `kernel` 
>> weights matrix        (see [regularizer](../regularizers.md)).    
>> bias_regularizer: Regularizer function applied to the bias vector        
>> (see [regularizer](../regularizers.md)).    activity_regularizer: 
>> Regularizer function applied to        the output of the layer (its 
>> "activation").        (see [regularizer](../regularizers.md)).    
>> kernel_constraint: Constraint function applied to        the `kernel` 
>> weights matrix        (see [constraints](../constraints.md)).    
>> bias_constraint: Constraint function applied to the bias vector        (see 
>> [constraints](../constraints.md)).# Input shape    nD tensor with shape: 
>> `(batch_size, ..., input_dim)`.    The most common situation would be    a 
>> 2D input with shape `(batch_size, input_dim)`.# Output shape    nD tensor 
>> with shape: `(batch_size, ..., units)`.    For instance, for a 2D input 
>> with shape `(batch_size, input_dim)`,    the output would have shape 
>> `(batch_size, units)`.File:           
>> c:\programdata\anaconda3\lib\site-packages\keras\layers\core.pyType:         
>>   
>> typeSubclasses:     *
>>
>> I did not choose any help topic. even though the above help message is 
>> comming at the end of output. 
>> Is this an error in the code or something to do with settings.
>>
>> Thanks and Regards,
>> Vivek
>>
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>>  
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>> .
>>
>

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